Researchers have turned the classic mystery board game Clue into a laboratory for testing whether AI agents can actually think through multi-step problems, or just look like they can.
A new study describes a text-based, multi-agent version of Clue built to evaluate deductive reasoning in large language models across extended interactions. The researchers set up six AI agents (three running GPT-4o-mini, three running Gemini-2.5-Flash) to play repeated games and establish a baseline. They then built a possibility matrix, a structured tool that explicitly tracks which suspects, weapons, and rooms remain possible after each turn, so an agent does not have to hold that bookkeeping inside its own generated reasoning text. The team compares agents using this tool against the unaided baseline to see whether offloading memory and constraint-tracking changes reasoning quality and game outcomes.
Clue is really a stand-in for a broader weakness: LLMs tend to lose track of constraints and contradict earlier conclusions once a task runs long enough, whether that is a support agent juggling policy rules or an autonomous system executing a multi-step plan. Testing whether an external structured record, rather than a bigger model or a cleverer prompt, changes the outcome gets at whether the bottleneck is a model's working memory or its logic itself.
If offloading memory to a simple data structure moves the needle, it's a useful reminder that some reasoning failures are really just filing problems in disguise.